Comparing static and dynamic aspects of patient flows via process model visualisations

Andrews, Robert D., Suriadi, Suriadi, Wynn, Moe T., ter Hofstede, Arthur H.M., Pika, Anastasiia, Nguyen, Huang Huy, & La Rosa, Marcello (2016) Comparing static and dynamic aspects of patient flows via process model visualisations.

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Abstract

Context

  • Healthcare processes such as patient flows in the emergency department, are notoriously complex because of their high degree of variability. Yet, hospital stakeholders need to understand differences between variants of these processes, e.g. between different patient cohorts or different times of the day, to offer a consistent and reliable service to their patients.

Objective

  • Analysis of these processes through classic process mining techniques is hampered by the inherently high variability of these processes.

Method

  • This paper contributes two novel visualisation and animation techniques, specifically catered for highly-varied business processes, which can distill both static and dynamic differences between variants of the same business process, using their execution logs.

Results

  • We validate these techniques via two case studies with two Australian hospitals and obtained insights on the differences that underpin patient flows for various patient cohorts through the Emergency Department.

Conclusion

  • The Configurable Model Visualisation technique provides a birds-eye view of similarities and differences be- tween process variants while the Log Animation tool addresses behavioural comparison and is unique in its ability to display concurrently the processing of multiple process variants through a single BMPN model.

Impact and interest:

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ID Code: 102848
Item Type: Report
Refereed: No
Keywords: patient flow, process mining, process variant, process comparison, Apromore
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > INFORMATION SYSTEMS (080600) > Information Engineering and Theory (080607)
Australian and New Zealand Standard Research Classification > MEDICAL AND HEALTH SCIENCES (110000) > PUBLIC HEALTH AND HEALTH SERVICES (111700) > Health Information Systems (incl. Surveillance) (111711)
Divisions: Current > Schools > School of Information Systems
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: 2017 The Author(s)
Deposited On: 16 Jan 2017 04:21
Last Modified: 05 Feb 2017 23:13

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